Figure REF  shows the system overview.
We train our system for a specific target object, in Figure REF  the drill.
The inputs to our system are the RGB color image, the depth image, and a segmentation mask indicating which pixels belong to the target object.
We first create a point cloud segment of the target object based on the inputs.
Each point has 6 dimensions: 3 dimensions for spatial coordinates and 3 dimensions for color information.
We randomly sample \(n\)  points from this point cloud segment to create a fixed-size downsampled point cloud.
In all of our experiments, we use \(n=256\) .
We then remove the estimated translation from the point coordinates to normalize the data.
The normalized point cloud segment is then fed into a network which outputs a rotation prediction in the axis-angle format.
During training, we use the ground truth segmentation and translation.
As we focus on the rotation estimation, during testing, we apply the segmentation and translation outputs of PoseCNN [1]}.
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